/**
Check whether the spectra for the given workspaces are the same.
@param ws1 : First workspace to compare
@param ws2 : Second workspace to compare against
exception. Otherwise a warning is generated.
*/
void ReflectometryReductionOne2::verifySpectrumMaps(
MatrixWorkspace_const_sptr ws1, MatrixWorkspace_const_sptr ws2) {
bool mismatch = false;
// Check that the number of histograms is the same
if (ws1->getNumberHistograms() != ws2->getNumberHistograms()) {
mismatch = true;
}
// Check that the spectrum numbers match for each histogram
if (!mismatch) {
for (size_t i = 0; i < ws1->getNumberHistograms(); ++i) {
if (ws1->indexInfo().spectrumNumber(i) !=
ws2->indexInfo().spectrumNumber(i)) {
mismatch = true;
break;
}
}
}
// Handle if error
if (mismatch) {
const std::string message =
"Spectrum maps between workspaces do NOT match up.";
g_log.warning(message);
}
}
/**
* Construct a DataSource object around the specifed MatrixWorkspace.
*
* @param matWs Shared pointer to the matrix workspace being "wrapped"
*/
MatrixWSDataSource::MatrixWSDataSource( MatrixWorkspace_const_sptr matWs ) :
SpectrumDataSource( 0.0, 1.0, 0.0, 1.0, 0, 0 ),
m_matWs(matWs),
m_emodeHandler(NULL)
{
m_totalXMin = matWs->getXMin();
m_totalXMax = matWs->getXMax();
m_totalYMin = 0; // Y direction is spectrum index
m_totalYMax = (double)matWs->getNumberHistograms();
m_totalRows = matWs->getNumberHistograms();
m_totalCols = 1000000; // Default data resolution
m_instrument = m_matWs->getInstrument();
if ( m_instrument )
{
m_source = m_instrument->getSource();
if ( !m_source )
{
g_log.debug("No SOURCE on instrument in MatrixWorkspace");
}
m_sample = m_instrument->getSample();
if ( !m_sample )
{
g_log.debug("No SAMPLE on instrument in MatrixWorkspace");
}
}
else
{
g_log.debug("No INSTRUMENT on MatrixWorkspace");
}
}
/** Initialise a workspace from its parent
* This sets values such as title, instrument, units, sample, spectramap.
* This does NOT copy any data.
*
* @param parent :: the parent workspace
* @param child :: the child workspace
* @param differentSize :: A flag to indicate if the two workspace will be different sizes
*/
void WorkspaceFactoryImpl::initializeFromParent(const MatrixWorkspace_const_sptr parent,
const MatrixWorkspace_sptr child, const bool differentSize) const
{
child->setTitle(parent->getTitle());
child->setComment(parent->getComment());
child->setInstrument(parent->getInstrument()); // This call also copies the SHARED POINTER to the parameter map
// This call will (should) perform a COPY of the parameter map.
child->instrumentParameters();
child->m_sample = parent->m_sample;
child->m_run = parent->m_run;
child->setYUnit(parent->m_YUnit);
child->setYUnitLabel(parent->m_YUnitLabel);
child->isDistribution(parent->isDistribution());
// Only copy the axes over if new sizes are not given
if ( !differentSize )
{
// Only copy mask map if same size for now. Later will need to check continued validity.
child->m_masks = parent->m_masks;
}
// Same number of histograms = copy over the spectra data
if (parent->getNumberHistograms() == child->getNumberHistograms())
{
for (size_t wi=0; wi<parent->getNumberHistograms(); wi++)
{
ISpectrum * childSpec = child->getSpectrum(wi);
const ISpectrum * parentSpec = parent->getSpectrum(wi);
// Copy spectrum number and detector IDs
childSpec->copyInfoFrom(*parentSpec);
}
}
// deal with axis
for (size_t i = 0; i < parent->m_axes.size(); ++i)
{
const size_t newAxisLength = child->getAxis(i)->length();
const size_t oldAxisLength = parent->getAxis(i)->length();
if ( !differentSize || newAxisLength == oldAxisLength )
{
// Need to delete the existing axis created in init above
delete child->m_axes[i];
// Now set to a copy of the parent workspace's axis
child->m_axes[i] = parent->m_axes[i]->clone(child.get());
}
else
{
if (! parent->getAxis(i)->isSpectra()) // WHY???
{
delete child->m_axes[i];
// Call the 'different length' clone variant
child->m_axes[i] = parent->m_axes[i]->clone(newAxisLength,child.get());
}
}
}
return;
}
/** Create a new instance of the same type of workspace as that given as
* argument.
* If the optional size parameters are given, the workspace will be initialised
* using
* those; otherwise it will be initialised to the same size as the parent.
* This method should be used when you want to carry over the Workspace data
* members
* relating to the Instrument, Spectra-Detector Map, Sample & Axes to the new
* workspace.
* If the workspace is the same size as its parent, then the X data, axes and
* mask list are
* copied. If its a different size then they are not.
* @param parent A shared pointer to the parent workspace
* @param NVectors (Optional) The number of vectors/histograms/detectors in
* the workspace
* @param XLength (Optional) The number of X data points/bin boundaries in
* each vector (must all be the same)
* @param YLength (Optional) The number of data/error points in each vector
* (must all be the same)
* @return A shared pointer to the newly created instance
* @throw std::out_of_range If invalid (0 or less) size arguments are given
* @throw NotFoundException If the class is not registered in the factory
*/
MatrixWorkspace_sptr
WorkspaceFactoryImpl::create(const MatrixWorkspace_const_sptr &parent,
size_t NVectors, size_t XLength,
size_t YLength) const {
bool differentSize(true);
// Use the parent sizes if new ones are not specified
if (NVectors == size_t(-1))
NVectors = parent->getNumberHistograms();
if (XLength == size_t(-1))
XLength = parent->dataX(0).size();
if (YLength == size_t(-1)) {
differentSize = false;
YLength = parent->blocksize();
}
// If the parent is an EventWorkspace, we want it to spawn a Workspace2D (or
// managed variant) as a child
std::string id(parent->id());
if (id == "EventWorkspace")
id = "Workspace2D";
// Create an 'empty' workspace of the appropriate type and size
MatrixWorkspace_sptr ws = create(id, NVectors, XLength, YLength);
// Copy over certain parent data members
initializeFromParent(parent, ws, differentSize);
return ws;
}
/**
* Execute the algorithm
*/
void ExtractMasking::exec()
{
MatrixWorkspace_const_sptr inputWS = getProperty("InputWorkspace");
const int nHist = static_cast<int>(inputWS->getNumberHistograms());
const int xLength(1), yLength(1);
// Create a new workspace for the results, copy from the input to ensure that we copy over the instrument and current masking
MatrixWorkspace_sptr outputWS = WorkspaceFactory::Instance().create(inputWS, nHist, xLength, yLength);
Progress prog(this,0.0,1.0,nHist);
MantidVecPtr xValues;
xValues.access() = MantidVec(1, 0.0);
PARALLEL_FOR2(inputWS, outputWS)
for( int i = 0; i < nHist; ++i )
{
PARALLEL_START_INTERUPT_REGION
// Spectrum in the output workspace
ISpectrum * outSpec = outputWS->getSpectrum(i);
// Spectrum in the input workspace
const ISpectrum * inSpec = inputWS->getSpectrum(i);
// Copy X, spectrum number and detector IDs
outSpec->setX(xValues);
outSpec->copyInfoFrom(*inSpec);
IDetector_const_sptr inputDet;
bool inputIsMasked(false);
try
{
inputDet = inputWS->getDetector(i);
if( inputDet->isMasked() )
{
inputIsMasked = true;
}
}
catch(Kernel::Exception::NotFoundError &)
{
inputIsMasked = false;
}
if( inputIsMasked )
{
outSpec->dataY()[0] = 0.0;
outSpec->dataE()[0] = 0.0;
}
else
{
outSpec->dataY()[0] = 1.0;
outSpec->dataE()[0] = 1.0;
}
prog.report();
PARALLEL_END_INTERUPT_REGION
}
PARALLEL_CHECK_INTERUPT_REGION
setProperty("OutputWorkspace", outputWS);
}
/** Executes the algorithm
*/
void MultiplyRange::exec()
{
// Get the input workspace and other properties
MatrixWorkspace_const_sptr inputWS = getProperty("InputWorkspace");
m_startBin = getProperty("StartBin");
m_endBin = getProperty("EndBin");
m_factor = getProperty("Factor");
// A few checks on the input properties
const int specSize = static_cast<int>(inputWS->blocksize());
if ( isEmpty(m_endBin) ) m_endBin = specSize - 1;
if ( m_endBin >= specSize )
{
g_log.error("EndBin out of range!");
throw std::out_of_range("EndBin out of range!");
}
if ( m_endBin < m_startBin )
{
g_log.error("StartBin must be less than or equal to EndBin");
throw std::out_of_range("StartBin must be less than or equal to EndBin");
}
// Only create the output workspace if it's different to the input one
MatrixWorkspace_sptr outputWS = getProperty("OutputWorkspace");
if ( outputWS != inputWS )
{
outputWS = WorkspaceFactory::Instance().create(inputWS);
setProperty("OutputWorkspace",outputWS);
}
// Get the count of histograms in the input workspace
const int histogramCount = static_cast<int>(inputWS->getNumberHistograms());
Progress progress(this,0.0,1.0,histogramCount);
// Loop over spectra
PARALLEL_FOR2(inputWS,outputWS)
for (int i = 0; i < histogramCount; ++i)
{
PARALLEL_START_INTERUPT_REGION
// Copy over the bin boundaries
outputWS->setX(i,inputWS->refX(i));
// Copy over the data
outputWS->dataY(i) = inputWS->readY(i);
outputWS->dataE(i) = inputWS->readE(i);
MantidVec& newY = outputWS->dataY(i);
MantidVec& newE = outputWS->dataE(i);
// Now multiply the requested range
std::transform(newY.begin()+m_startBin,newY.begin()+m_endBin+1,newY.begin()+m_startBin,
std::bind2nd(std::multiplies<double>(),m_factor));
std::transform(newE.begin()+m_startBin,newE.begin()+m_endBin+1,newE.begin()+m_startBin,
std::bind2nd(std::multiplies<double>(),m_factor));
progress.report();
PARALLEL_END_INTERUPT_REGION
}
PARALLEL_CHECK_INTERUPT_REGION
}
/**
* Perform validation of input properties:
* - input workspace must not be empty
* - X values must be evenly spaced (unless accepting rounding errors)
* - Real and Imaginary spectra must be in range of input workspace
* - If complex, real and imaginary workspaces must be the same size
* @returns :: map of property names to errors (empty map if no errors)
*/
std::map<std::string, std::string> FFT::validateInputs() {
std::map<std::string, std::string> errors;
MatrixWorkspace_const_sptr inWS = getProperty("InputWorkspace");
if (inWS) {
const int iReal = getProperty("Real");
const int iImag = getProperty("Imaginary");
const MantidVec &X = inWS->readX(iReal);
// check that the workspace isn't empty
if (X.size() < 2) {
errors["InputWorkspace"] =
"Input workspace must have at least two values";
} else {
// Check that the x values are evenly spaced
// If accepting rounding errors, just give a warning if bins are
// different.
if (areBinWidthsUneven(X)) {
errors["InputWorkspace"] =
"X axis must be linear (all bins have same width)";
}
}
// check real, imaginary spectrum numbers and workspace sizes
int nHist = static_cast<int>(inWS->getNumberHistograms());
if (iReal >= nHist) {
errors["Real"] = "Real out of range";
}
if (iImag != EMPTY_INT()) {
MatrixWorkspace_const_sptr inImagWS = getProperty("InputImagWorkspace");
if (inImagWS) {
if (inWS->blocksize() != inImagWS->blocksize()) {
errors["Imaginary"] = "Real and Imaginary sizes do not match";
}
nHist = static_cast<int>(inImagWS->getNumberHistograms());
}
if (iImag >= nHist) {
errors["Imaginary"] = "Imaginary out of range";
}
}
}
return errors;
}
void SumRowColumn::exec()
{
// First task is to integrate the input workspace
MatrixWorkspace_const_sptr integratedWS = integrateWorkspace();
const size_t numSpec = integratedWS->getNumberHistograms();
// Check number of spectra is 128*128 or 192*192. Print warning if not.
if (numSpec != 16384 && numSpec != 36864)
{
g_log.warning() << "The input workspace has " << numSpec << " spectra."
<< "This is not 128*128 or 192*192 - did you make a mistake?\n";
}
// This is the dimension if all rows/columns are included
const int dim = static_cast<int>( std::sqrt(static_cast<double>(numSpec)) );
// Check the column range properties
int start = getProperty("HOverVMin");
int end = getProperty("HOverVMax");
if ( isEmpty(start) ) start = 0;
if ( isEmpty(end) || end > dim-1 ) end = dim-1;
if ( start > end )
{
g_log.error("H/V_Min must be less than H/V_Max");
throw std::out_of_range("H/V_Min must be less than H/V_Max");
}
MatrixWorkspace_sptr outputWS = WorkspaceFactory::Instance().create(integratedWS,1,dim,dim);
// Remove the unit
outputWS->getAxis(0)->unit().reset();
// Get references to the vectors for the results
MantidVec& X = outputWS->dataX(0);
MantidVec& Y = outputWS->dataY(0);
// Get the orientation
const std::string orientation = getProperty("Orientation");
const bool horizontal = ( orientation=="D_H" ? 1 : 0 );
Progress progress(this,0,1,dim);
for (int i = 0; i < dim; ++i)
{
// Copy X values
X[i] = i;
// Now loop over calculating Y's
for (int j = start; j <= end; ++j)
{
const int index = ( horizontal ? ( i + j*dim) : ( i*dim + j) );
Y[i] += integratedWS->readY(index)[0];
}
}
setProperty("OutputWorkspace",outputWS);
}
std::map<std::string, std::string> EditInstrumentGeometry::validateInputs() {
std::map<std::string, std::string> result;
// everything depends on being parallel to the workspace # histo
size_t numHist(0);
bool hasWorkspacePtr(false);
{
MatrixWorkspace_const_sptr workspace = getProperty("Workspace");
// this is to guard against WorkspaceGroups
// fallthrough is to skip workspace check and make sure
if (bool(workspace)) {
hasWorkspacePtr = true;
numHist = workspace->getNumberHistograms();
}
}
std::string error;
const std::vector<int32_t> specids = this->getProperty("SpectrumIDs");
if (!hasWorkspacePtr) {
// use the number of spectra for the number of histograms
numHist = specids.size();
// give up if it is empty
if (numHist == 0) {
return result;
}
}
error = checkValues(specids, numHist);
if (!error.empty())
result["SpectrumIDs"] = error;
const std::vector<double> l2 = this->getProperty("L2");
error = checkValues(l2, numHist);
if (!error.empty())
result["L2"] = error;
const std::vector<double> tth = this->getProperty("Polar");
error = checkValues(tth, numHist);
if (!error.empty())
result["Polar"] = error;
const std::vector<double> phi = this->getProperty("Azimuthal");
error = checkValues(phi, numHist);
if (!error.empty())
result["Azimuthal"] = error;
const vector<int> detids = getProperty("DetectorIDs");
error = checkValues(detids, numHist);
if (!error.empty())
result["DetectorIDs"] = error;
// TODO verify that SpectrumIDs, L2, Polar, Azimuthal, and DetectorIDs are
// parallel or not specified
return result;
}
/** Get the list of workspace indices to use
*
* @param spec :: returns the list of workspace indices
*/
void SaveNexusProcessed::getSpectrumList(std::vector<int> & spec, MatrixWorkspace_const_sptr matrixWorkspace)
{
std::vector<int> spec_list = getProperty("WorkspaceIndexList");
int spec_min = getProperty("WorkspaceIndexMin");
int spec_max = getProperty("WorkspaceIndexMax");
const bool list = !spec_list.empty();
const bool interval = (spec_max != Mantid::EMPTY_INT());
if ( spec_max == Mantid::EMPTY_INT() ) spec_max = 0;
const int numberOfHist = static_cast<int>(matrixWorkspace->getNumberHistograms());
if( interval )
{
if ( spec_max < spec_min || spec_max > numberOfHist-1 )
{
g_log.error("Invalid WorkspaceIndex min/max properties");
throw std::invalid_argument("Inconsistent properties defined");
}
spec.reserve(1+spec_max-spec_min);
for(int i=spec_min;i<=spec_max;i++)
spec.push_back(i);
if (list)
{
for(size_t i=0;i<spec_list.size();i++)
{
int s = spec_list[i];
if ( s < 0 ) continue;
if (s < spec_min || s > spec_max)
spec.push_back(s);
}
}
}
else if (list)
{
spec_max=0;
spec_min=numberOfHist-1;
for(size_t i=0;i<spec_list.size();i++)
{
int s = spec_list[i];
if ( s < 0 ) continue;
spec.push_back(s);
if (s > spec_max) spec_max = s;
if (s < spec_min) spec_min = s;
}
}
else
{
spec_min=0;
spec_max=numberOfHist-1;
spec.reserve(1+spec_max-spec_min);
for(int i=spec_min;i<=spec_max;i++)
spec.push_back(i);
}
}
/** Validates input properties.
* @return A map of input properties as keys and (error) messages as values.
*/
std::map<std::string, std::string> NormaliseToMonitor::validateInputs() {
std::map<std::string, std::string> issues;
// Check where the monitor spectrum should come from
const Property *monSpecProp = getProperty("MonitorSpectrum");
const Property *monIDProp = getProperty("MonitorID");
MatrixWorkspace_const_sptr monWS = getProperty("MonitorWorkspace");
// something has to be set
if (monSpecProp->isDefault() && !monWS && monIDProp->isDefault()) {
const std::string mess("Either MonitorSpectrum, MonitorID or "
"MonitorWorkspace has to be provided.");
issues["MonitorSpectrum"] = mess;
issues["MonitorID"] = mess;
issues["MonitorWorkspace"] = mess;
}
const double intMin = getProperty("IntegrationRangeMin");
const double intMax = getProperty("IntegrationRangeMax");
if (!isEmpty(intMin) && !isEmpty(intMax)) {
if (intMin > intMax) {
issues["IntegrationRangeMin"] =
"Range minimum set to a larger value than maximum.";
issues["IntegrationRangeMax"] =
"Range maximum set to a smaller value than minimum.";
}
}
if (monWS && monSpecProp->isDefault()) {
const int monIndex = getProperty("MonitorWorkspaceIndex");
if (monIndex < 0) {
issues["MonitorWorkspaceIndex"] = "A workspace index cannot be negative.";
} else if (monWS->getNumberHistograms() <= static_cast<size_t>(monIndex)) {
issues["MonitorWorkspaceIndex"] =
"The MonitorWorkspace must contain the MonitorWorkspaceIndex.";
}
MatrixWorkspace_const_sptr inWS = getProperty("InputWorkspace");
if (monWS->getInstrument()->getName() != inWS->getInstrument()->getName()) {
issues["MonitorWorkspace"] = "The Input and Monitor workspaces must come "
"from the same instrument.";
}
if (monWS->getAxis(0)->unit()->unitID() !=
inWS->getAxis(0)->unit()->unitID()) {
issues["MonitorWorkspace"] =
"The Input and Monitor workspaces must have the same unit";
}
}
return issues;
}
/**
* Determine the minimum and maximum spectra ids.
*
* @param axis The axis to search through.
* @param min The minimum id (output).
* @param max The maximum id (output).
*/
void getMinMax(MatrixWorkspace_const_sptr ws, specid_t& min, specid_t& max)
{
specid_t temp;
size_t length = ws->getNumberHistograms();
// initial values
min = max = ws->getSpectrum(0)->getSpectrumNo();
for (size_t i = 0; i < length; i++)
{
temp = ws->getSpectrum(i)->getSpectrumNo();
// Adjust min/max
if (temp < min)
min = temp;
if (temp > max)
max = temp;
}
}
/*
Execute the transformtion. Generates an output IMDEventWorkspace.
@return the constructed IMDEventWorkspace following the transformation.
@param ws: Input MatrixWorkspace const shared pointer
*/
IMDEventWorkspace_sptr ReflectometryTransformQxQz::execute(MatrixWorkspace_const_sptr inputWs) const
{
const size_t nbinsx = 10;
const size_t nbinsz = 10;
auto ws = boost::make_shared<MDEventWorkspace<MDLeanEvent<2>,2> >();
MDHistoDimension_sptr qxDim = MDHistoDimension_sptr(new MDHistoDimension("Qx","qx","(Ang^-1)", static_cast<Mantid::coord_t>(m_qxMin), static_cast<Mantid::coord_t>(m_qxMax), nbinsx));
MDHistoDimension_sptr qzDim = MDHistoDimension_sptr(new MDHistoDimension("Qz","qz","(Ang^-1)", static_cast<Mantid::coord_t>(m_qzMin), static_cast<Mantid::coord_t>(m_qzMax), nbinsz));
ws->addDimension(qxDim);
ws->addDimension(qzDim);
// Set some reasonable values for the box controller
BoxController_sptr bc = ws->getBoxController();
bc->setSplitInto(2);
bc->setSplitThreshold(10);
// Initialize the workspace.
ws->initialize();
// Start with a MDGridBox.
ws->splitBox();
auto spectraAxis = inputWs->getAxis(1);
for(size_t index = 0; index < inputWs->getNumberHistograms(); ++index)
{
auto counts = inputWs->readY(index);
auto wavelengths = inputWs->readX(index);
auto errors = inputWs->readE(index);
const size_t nInputBins = wavelengths.size() -1;
const double theta_final = spectraAxis->getValue(index);
m_QxCalculation.setThetaFinal(theta_final);
m_QzCalculation.setThetaFinal(theta_final);
//Loop over all bins in spectra
for(size_t binIndex = 0; binIndex < nInputBins; ++binIndex)
{
const double& wavelength = 0.5*(wavelengths[binIndex] + wavelengths[binIndex+1]);
double _qx = m_QxCalculation.execute(wavelength);
double _qz = m_QzCalculation.execute(wavelength);
double centers[2] = {_qx, _qz};
ws->addEvent(MDLeanEvent<2>(float(counts[binIndex]), float(errors[binIndex]*errors[binIndex]), centers));
}
ws->splitAllIfNeeded(NULL);
}
return ws;
}
void ConvertToMatrixWorkspace::exec()
{
MatrixWorkspace_const_sptr inputWorkspace = getProperty("InputWorkspace");
// Let's see if we have to do anything first. Basically we want to avoid the data copy if we can
DataObjects::EventWorkspace_const_sptr eventW =
boost::dynamic_pointer_cast<const DataObjects::EventWorkspace>(inputWorkspace);
MatrixWorkspace_sptr outputWorkspace;
if( eventW )
{
g_log.information() << "Converting EventWorkspace to Workspace2D.\n";
const size_t numHists = inputWorkspace->getNumberHistograms();
Progress prog(this,0.0,1.0,numHists*2);
// Sort the input workspace in-place by TOF. This can be faster if there are few event lists.
eventW->sortAll(TOF_SORT, &prog);
// Create the output workspace. This will copy many aspects fron the input one.
outputWorkspace = WorkspaceFactory::Instance().create(inputWorkspace);
// ...but not the data, so do that here.
PARALLEL_FOR2(inputWorkspace,outputWorkspace)
for (int64_t i = 0; i < (int64_t)numHists; ++i)
{
PARALLEL_START_INTERUPT_REGION
const ISpectrum * inSpec = inputWorkspace->getSpectrum(i);
ISpectrum * outSpec = outputWorkspace->getSpectrum(i);
outSpec->copyInfoFrom(*inSpec);
outSpec->setX(inSpec->ptrX());
outSpec->dataY() = inSpec->dataY();
outSpec->dataE() = inSpec->dataE();
prog.report("Binning");
PARALLEL_END_INTERUPT_REGION
}
PARALLEL_CHECK_INTERUPT_REGION
outputWorkspace->generateSpectraMap();
}
else
{
/**
* Plots the loaded file to the miniplot and sets the guides
* and the range
*
* @param filename :: The name of the workspace to plot
*/
void Stretch::handleSampleInputReady(const QString &filename) {
m_uiForm.ppPlot->addSpectrum("Sample", filename, 0);
// update the maximum and minimum range bar positions
QPair<double, double> range = m_uiForm.ppPlot->getCurveRange("Sample");
auto eRangeSelector = m_uiForm.ppPlot->getRangeSelector("StretchERange");
setRangeSelector(eRangeSelector, m_properties["EMin"], m_properties["EMax"],
range);
setPlotPropertyRange(eRangeSelector, m_properties["EMin"],
m_properties["EMax"], range);
// update the current positions of the range bars
eRangeSelector->setMinimum(range.first);
eRangeSelector->setMaximum(range.second);
// set the max spectrum
MatrixWorkspace_const_sptr sampleWs =
getADSMatrixWorkspace(filename.toStdString());
const int spectra = static_cast<int>(sampleWs->getNumberHistograms());
m_uiForm.spPreviewSpectrum->setMaximum(spectra - 1);
}
/**
* Add -1.*minValue on each spectra.
*
* @param minWS A workspace of minimum values for each spectra. This is
*calculated in
* the @see exec portion of the algorithm.
* @param wksp The workspace to modify.
* @param prog The progress.
*/
void ResetNegatives::pushMinimum(MatrixWorkspace_const_sptr minWS,
MatrixWorkspace_sptr wksp, Progress &prog) {
int64_t nHist = minWS->getNumberHistograms();
PARALLEL_FOR_IF(Kernel::threadSafe(*wksp, *minWS))
for (int64_t i = 0; i < nHist; i++) {
PARALLEL_START_INTERUPT_REGION
double minValue = minWS->y(i)[0];
if (minValue <= 0) {
minValue *= -1.;
auto &y = wksp->mutableY(i);
for (double &value : y) {
value = fixZero(value + minValue);
}
}
prog.report();
PARALLEL_END_INTERUPT_REGION
}
PARALLEL_CHECK_INTERUPT_REGION
}
/**
* Determine the minimum spectrum id for summing. This requires that
* SumSpectra::indices has already been set.
* @param localworkspace The workspace to use.
* @return The minimum spectrum id for all the spectra being summed.
*/
specid_t
SumSpectra::getOutputSpecId(MatrixWorkspace_const_sptr localworkspace) {
// initial value
specid_t specId =
localworkspace->getSpectrum(*(this->m_indices.begin()))->getSpectrumNo();
// the total number of spectra
int totalSpec = static_cast<int>(localworkspace->getNumberHistograms());
specid_t temp;
for (auto index : this->m_indices) {
if (index < totalSpec) {
temp = localworkspace->getSpectrum(index)->getSpectrumNo();
if (temp < specId)
specId = temp;
}
}
return specId;
}
void FlatBackground::exec()
{
// Retrieve the input workspace
MatrixWorkspace_const_sptr inputWS = getProperty("InputWorkspace");
// Copy over all the data
const int numHists = static_cast<int>(inputWS->getNumberHistograms());
const int blocksize = static_cast<int>(inputWS->blocksize());
// Get the required X range
double startX,endX;
this->checkRange(startX,endX);
std::vector<int> specInds = getProperty("WorkspaceIndexList");
// check if the user passed an empty list, if so all of spec will be processed
this->getSpecInds(specInds, numHists);
// Are we removing the background?
const bool removeBackground =
std::string(getProperty("outputMode")) == "Subtract Background";
// Initialise the progress reporting object
m_progress = new Progress(this,0.0,0.2,numHists);
MatrixWorkspace_sptr outputWS = getProperty("OutputWorkspace");
// If input and output workspaces are not the same, create a new workspace for the output
if (outputWS != inputWS )
{
outputWS = WorkspaceFactory::Instance().create(inputWS);
PARALLEL_FOR2(inputWS,outputWS)
for (int i = 0; i < numHists; ++i)
{
PARALLEL_START_INTERUPT_REGION
outputWS->dataX(i) = inputWS->readX(i);
outputWS->dataY(i) = inputWS->readY(i);
outputWS->dataE(i) = inputWS->readE(i);
m_progress->report();
PARALLEL_END_INTERUPT_REGION
}
PARALLEL_CHECK_INTERUPT_REGION
}
/**
* Add -1.*minValue on each spectra.
*
* @param minWS A workspace of minimum values for each spectra. This is calculated in
* the @see exec portion of the algorithm.
* @param wksp The workspace to modify.
* @param prog The progress.
*/
void ResetNegatives::pushMinimum(MatrixWorkspace_const_sptr minWS, MatrixWorkspace_sptr wksp, Progress &prog)
{
int64_t nHist = minWS->getNumberHistograms();
PARALLEL_FOR2(wksp, minWS)
for (int64_t i = 0; i < nHist; i++)
{
PARALLEL_START_INTERUPT_REGION
double minValue = minWS->readY(i)[0];
if (minValue <= 0)
{
minValue *= -1.;
MantidVec & y = wksp->dataY(i);
for (MantidVec::iterator it = y.begin(); it != y.end(); ++it)
{
*it = fixZero(*it + minValue);
}
}
prog.report();
PARALLEL_END_INTERUPT_REGION
}
PARALLEL_CHECK_INTERUPT_REGION
}
void SofQW::exec() {
// Find the approopriate algorithm
std::string method = this->getProperty("Method");
std::string child = "SofQW" + method;
// Setup and run
Algorithm_sptr childAlg = boost::dynamic_pointer_cast<Algorithm>(
createChildAlgorithm(child, 0.0, 1.0));
// This will add the Method property to the child algorithm but it will be
// ignored anyway...
childAlg->copyPropertiesFrom(*this);
childAlg->execute();
MatrixWorkspace_sptr outputWS = childAlg->getProperty("OutputWorkspace");
this->setProperty("OutputWorkspace", outputWS);
// Progress reports & cancellation
MatrixWorkspace_const_sptr inputWorkspace = getProperty("InputWorkspace");
const size_t nHistos = inputWorkspace->getNumberHistograms();
auto m_progress = make_unique<Progress>(this, 0.0, 1.0, nHistos);
m_progress->report("Creating output workspace");
}
IMDHistoWorkspace_sptr ReflectometryTransform::executeMDNormPoly(
MatrixWorkspace_const_sptr inputWs) const {
auto input_x_dim = inputWs->getXDimension();
MDHistoDimension_sptr dim0 = MDHistoDimension_sptr(new MDHistoDimension(
input_x_dim->getName(), input_x_dim->getDimensionId(),
input_x_dim->getMDFrame(),
static_cast<Mantid::coord_t>(input_x_dim->getMinimum()),
static_cast<Mantid::coord_t>(input_x_dim->getMaximum()),
input_x_dim->getNBins()));
auto input_y_dim = inputWs->getYDimension();
MDHistoDimension_sptr dim1 = MDHistoDimension_sptr(new MDHistoDimension(
input_y_dim->getName(), input_y_dim->getDimensionId(),
input_y_dim->getMDFrame(),
static_cast<Mantid::coord_t>(input_y_dim->getMinimum()),
static_cast<Mantid::coord_t>(input_y_dim->getMaximum()),
input_y_dim->getNBins()));
auto outWs = boost::make_shared<MDHistoWorkspace>(dim0, dim1);
for (size_t nHistoIndex = 0; nHistoIndex < inputWs->getNumberHistograms();
++nHistoIndex) {
const MantidVec X = inputWs->readX(nHistoIndex);
const MantidVec Y = inputWs->readY(nHistoIndex);
const MantidVec E = inputWs->readE(nHistoIndex);
for (size_t nBinIndex = 0; nBinIndex < inputWs->blocksize(); ++nBinIndex) {
auto value_index = outWs->getLinearIndex(nBinIndex, nHistoIndex);
outWs->setSignalAt(value_index, Y[nBinIndex]);
outWs->setErrorSquaredAt(value_index, E[nBinIndex] * E[nBinIndex]);
}
}
return outWs;
}
void SANSDirectBeamScaling::exec()
{
MatrixWorkspace_const_sptr inputWS = getProperty("InputWorkspace");
const double beamRadius = getProperty("BeamRadius");
const double attTrans = getProperty("AttenuatorTransmission");
const double attTransErr = getProperty("AttenuatorTransmissionError");
// Extract the required spectra into separate workspaces
std::vector<detid_t> udet;
std::vector<size_t> index;
udet.push_back(getProperty("BeamMonitor"));
// Convert UDETs to workspace indices
inputWS->getIndicesFromDetectorIDs(udet,index);
if (index.size() < 1)
{
g_log.debug() << "inputWS->getIndicesFromDetectorIDs() returned empty\n";
throw std::invalid_argument("Could not find the incident beam monitor spectra\n");
}
const int64_t numHists = inputWS->getNumberHistograms();
Progress progress(this,0.0,1.0,numHists);
// Number of X bins
const int64_t xLength = inputWS->readY(0).size();
// Monitor counts
double monitor = 0.0;
const MantidVec& MonIn = inputWS->readY(index[0]);
for (int64_t j = 0; j < xLength; j++)
monitor += MonIn[j];
const V3D sourcePos = inputWS->getInstrument()->getSource()->getPos();
double counts = 0.0;
double error = 0.0;
int nPixels = 0;
// Sample-detector distance for the contributing pixels
double sdd = 0.0;
for (int64_t i = 0; i < int64_t(numHists); ++i)
{
IDetector_const_sptr det;
try {
det = inputWS->getDetector(i);
} catch (Exception::NotFoundError&) {
g_log.warning() << "Spectrum index " << i << " has no detector assigned to it - discarding" << std::endl;
continue;
}
// Skip if we have a monitor or if the detector is masked.
if ( det->isMonitor() || det->isMasked() ) continue;
const MantidVec& YIn = inputWS->readY(i);
const MantidVec& EIn = inputWS->readE(i);
// Sum up all the counts
V3D pos = det->getPos() - V3D(sourcePos.X(), sourcePos.Y(), 0.0);
const double pixelDistance = pos.Z();
pos.setZ(0.0);
if (pos.norm() <= beamRadius) {
// Correct data for all X bins
for (int64_t j = 0; j < xLength; j++)
{
counts += YIn[j];
error += EIn[j]*EIn[j];
}
nPixels += 1;
sdd += pixelDistance;
}
progress.report("Absolute Scaling");
}
// Get the average SDD for the counted pixels, and transform to mm.
sdd = sdd/nPixels*1000.0;
error = std::sqrt(error);
// Transform from m to mm
double sourceAperture = getProperty("SourceApertureRadius");
sourceAperture *= 1000.0;
// Transform from m to mm
double sampleAperture = getProperty("SampleApertureRadius");
sampleAperture *= 1000.0;
//TODO: replace this by some meaningful value
const double KCG2FluxPerMon_SUGAR = 1.0;
// Solid angle correction scale in 1/(cm^2)/steradian
double solidAngleCorrScale = sdd/(M_PI*sourceAperture*sampleAperture);
solidAngleCorrScale = solidAngleCorrScale*solidAngleCorrScale*100.0;
// Scaling factor in n/(monitor count)/(cm^2)/steradian
double scale = counts/monitor*solidAngleCorrScale/KCG2FluxPerMon_SUGAR;
double scaleErr = std::abs(error/monitor)*solidAngleCorrScale/KCG2FluxPerMon_SUGAR;
scaleErr = std::abs(scale/attTrans)*sqrt( (scaleErr/scale)*(scaleErr/scale) +(attTransErr/attTrans)*(attTransErr/attTrans) );
scale /= attTrans;
std::vector<double> output;
output.push_back(scale);
output.push_back(scaleErr);
setProperty("ScaleFactor", output);
}
/**
* Execution path for NormalisedPolygon Rebinning
* @param inputWS : Workspace to be rebinned
* @param vertexes : TableWorkspace for debugging purposes
* @param dumpVertexes : determines whether vertexes will be written to for
* debugging purposes or not
* @param outputDimensions : used for the column headings for Dump Vertexes
*/
MatrixWorkspace_sptr ReflectometryTransform::executeNormPoly(
MatrixWorkspace_const_sptr inputWS,
boost::shared_ptr<Mantid::DataObjects::TableWorkspace> &vertexes,
bool dumpVertexes, std::string outputDimensions) const {
MatrixWorkspace_sptr temp = WorkspaceFactory::Instance().create(
"RebinnedOutput", m_d1NumBins, m_d0NumBins, m_d0NumBins);
RebinnedOutput_sptr outWS = boost::static_pointer_cast<RebinnedOutput>(temp);
const double widthD0 = (m_d0Max - m_d0Min) / double(m_d0NumBins);
const double widthD1 = (m_d1Max - m_d1Min) / double(m_d1NumBins);
std::vector<double> xBinsVec;
std::vector<double> zBinsVec;
VectorHelper::createAxisFromRebinParams({m_d1Min, widthD1, m_d1Max},
zBinsVec);
VectorHelper::createAxisFromRebinParams({m_d0Min, widthD0, m_d0Max},
xBinsVec);
// Put the correct bin boundaries into the workspace
auto verticalAxis = new BinEdgeAxis(zBinsVec);
outWS->replaceAxis(1, verticalAxis);
for (size_t i = 0; i < zBinsVec.size() - 1; ++i)
outWS->setX(i, xBinsVec);
verticalAxis->title() = m_d1Label;
// Prepare the required theta values
DetectorAngularCache cache = initAngularCaches(inputWS.get());
m_theta = cache.thetas;
m_thetaWidths = cache.thetaWidths;
const size_t nHistos = inputWS->getNumberHistograms();
const size_t nBins = inputWS->blocksize();
// Holds the spectrum-detector mapping
std::vector<specnum_t> specNumberMapping;
std::vector<detid_t> detIDMapping;
// Create a table for the output if we want to debug vertex positioning
addColumnHeadings(vertexes, outputDimensions);
for (size_t i = 0; i < nHistos; ++i) {
IDetector_const_sptr detector = inputWS->getDetector(i);
if (!detector || detector->isMasked() || detector->isMonitor()) {
continue;
}
// Compute polygon points
const double theta = m_theta[i];
const double thetaWidth = m_thetaWidths[i];
const double thetaHalfWidth = 0.5 * thetaWidth;
const double thetaLower = theta - thetaHalfWidth;
const double thetaUpper = theta + thetaHalfWidth;
const MantidVec &X = inputWS->readX(i);
const MantidVec &Y = inputWS->readY(i);
const MantidVec &E = inputWS->readE(i);
for (size_t j = 0; j < nBins; ++j) {
const double lamLower = X[j];
const double lamUpper = X[j + 1];
const double signal = Y[j];
const double error = E[j];
auto inputQ =
m_calculator->createQuad(lamUpper, lamLower, thetaUpper, thetaLower);
FractionalRebinning::rebinToFractionalOutput(inputQ, inputWS, i, j, outWS,
zBinsVec);
// Find which qy bin this point lies in
const auto qIndex =
std::upper_bound(zBinsVec.begin(), zBinsVec.end(), inputQ[0].Y()) -
zBinsVec.begin();
if (qIndex != 0 && qIndex < static_cast<int>(zBinsVec.size())) {
// Add this spectra-detector pair to the mapping
specNumberMapping.push_back(
outWS->getSpectrum(qIndex - 1).getSpectrumNo());
detIDMapping.push_back(detector->getID());
}
// Debugging
if (dumpVertexes) {
writeRow(vertexes, inputQ[0], i, j, signal, error);
writeRow(vertexes, inputQ[1], i, j, signal, error);
writeRow(vertexes, inputQ[2], i, j, signal, error);
writeRow(vertexes, inputQ[3], i, j, signal, error);
}
}
}
outWS->finalize();
FractionalRebinning::normaliseOutput(outWS, inputWS);
// Set the output spectrum-detector mapping
SpectrumDetectorMapping outputDetectorMap(specNumberMapping, detIDMapping);
outWS->updateSpectraUsing(outputDetectorMap);
outWS->getAxis(0)->title() = m_d0Label;
outWS->setYUnit("");
outWS->setYUnitLabel("Intensity");
return outWS;
}
/** Executes the algorithm
*
* @throw Exception::FileError If the grouping file cannot be opened or read successfully
* @throw runtime_error If unable to run one of the Child Algorithms successfully
*/
void ReadGroupsFromFile::exec()
{
MatrixWorkspace_const_sptr ws = getProperty("InstrumentWorkspace");
// Get the instrument.
Instrument_const_sptr inst = ws->getInstrument();
// Create a copy (without the data) of the workspace - it will contain the
Workspace2D_sptr localWorkspace =
boost::dynamic_pointer_cast<Workspace2D>(WorkspaceFactory::Instance().create(ws, ws->getNumberHistograms(), 2, 1));
if (!localWorkspace)
throw std::runtime_error("Failed when creating a Workspace2D from the input!");
const std::string groupfile=getProperty("GroupingFilename");
if ( ! groupfile.empty() )
{
std::string filename(groupfile);
std::transform(filename.begin(), filename.end(), filename.begin(), tolower);
if ( filename.find(".xml") != std::string::npos )
{
readXMLGroupingFile(groupfile);
}
else
{
readGroupingFile(groupfile);
}
}
// Get the instrument.
const int64_t nHist=localWorkspace->getNumberHistograms();
// Determine whether the user wants to see unselected detectors or not
const std::string su=getProperty("ShowUnselected");
bool showunselected=(!su.compare("True"));
bool success=false;
for (int64_t i=0;i<nHist;i++)
{
ISpectrum * spec = localWorkspace->getSpectrum(i);
const std::set<detid_t> & dets = spec->getDetectorIDs();
if (dets.empty()) // Nothing
{
spec->dataY()[0]=0.0;
continue;
}
// Find the first detector ID in the list
calmap::const_iterator it=calibration.find(*dets.begin());
if (it==calibration.end()) //Could not find the detector
{
spec->dataY()[0]=0.0;
continue;
}
if (showunselected)
{
if (((*it).second).second==0)
spec->dataY()[0]=0.0;
else
spec->dataY()[0]=static_cast<double>(((*it).second).first);
}
else
spec->dataY()[0]=static_cast<double>(((*it).second).first);
if (!success) success=true; //At least one detector is found in the cal file
}
progress(1);
calibration.clear();
if (!success) //Do some cleanup
{
localWorkspace.reset();
throw std::runtime_error("Fail to found a detector in "+groupfile+" existing in instrument "+inst->getName());
}
setProperty("OutputWorkspace",localWorkspace);
return;
}
void GroupDetectors2::exec()
{
// Get the input workspace
const MatrixWorkspace_const_sptr inputWS = getProperty("InputWorkspace");
//Check if it is an event workspace
const bool preserveEvents = getProperty("PreserveEvents");
EventWorkspace_const_sptr eventW = boost::dynamic_pointer_cast<const EventWorkspace>(inputWS);
if (eventW != NULL && preserveEvents)
{
this->execEvent();
return;
}
const size_t numInHists = inputWS->getNumberHistograms();
// Bin boundaries need to be the same, so do the full check on whether they actually are
if (!API::WorkspaceHelpers::commonBoundaries(inputWS))
{
g_log.error() << "Can only group if the histograms have common bin boundaries\n";
throw std::invalid_argument("Can only group if the histograms have common bin boundaries");
}
progress( m_FracCompl = CHECKBINS );
interruption_point();
// some values loaded into this vector can be negative so this needs to be a signed type
std::vector<int64_t> unGroupedInds;
//the ungrouped list could be very big but might be none at all
unGroupedInds.reserve(numInHists);
for( size_t i = 0; i < numInHists ; i++ )
{
unGroupedInds.push_back(i);
}
// read in the input parameters to make that map, if KeepUngroupedSpectra was set we'll need a list of the ungrouped spectrra too
getGroups(inputWS, unGroupedInds);
// converting the list into a set gets rid of repeated values, here the multiple GroupDetectors2::USED become one USED at the start
const std::set<int64_t> unGroupedSet(unGroupedInds.begin(), unGroupedInds.end());
// Check what the user asked to be done with ungrouped spectra
const bool keepAll = getProperty("KeepUngroupedSpectra");
// ignore the one USED value in set or ignore all the ungrouped if the user doesn't want them
const size_t numUnGrouped = keepAll ? unGroupedSet.size()-1 : 0;
MatrixWorkspace_sptr outputWS =
WorkspaceFactory::Instance().create(inputWS, m_GroupSpecInds.size()+ numUnGrouped,
inputWS->readX(0).size(), inputWS->blocksize());
// prepare to move the requested histograms into groups, first estimate how long for progress reporting. +1 in the demonator gets rid of any divide by zero risk
double prog4Copy=( (1.0 - m_FracCompl)/(static_cast<double>(numInHists-unGroupedSet.size())+1.) )*
(keepAll ? static_cast<double>(numInHists-unGroupedSet.size())/static_cast<double>(numInHists): 1.);
// Build a new map
const size_t outIndex = formGroups(inputWS, outputWS, prog4Copy);
// If we're keeping ungrouped spectra
if (keepAll)
{
// copy them into the output workspace
moveOthers(unGroupedSet, inputWS, outputWS, outIndex);
}
g_log.information() << name() << " algorithm has finished\n";
setProperty("OutputWorkspace",outputWS);
}
/** Executes the algorithm
*
* @throw runtime_error Thrown if algorithm cannot execute
*/
void MaxMin::exec()
{
// Try and retrieve the optional properties
m_MinRange = getProperty("RangeLower");
m_MaxRange = getProperty("RangeUpper");
m_MinSpec = getProperty("StartWorkspaceIndex");
m_MaxSpec = getProperty("EndWorkspaceIndex");
showMin = getProperty("ShowMin");
// Get the input workspace
MatrixWorkspace_const_sptr localworkspace = getProperty("InputWorkspace");
const int numberOfSpectra = static_cast<int>(localworkspace->getNumberHistograms());
// Check 'StartSpectrum' is in range 0-numberOfSpectra
if ( m_MinSpec > numberOfSpectra )
{
g_log.warning("StartSpectrum out of range! Set to 0.");
m_MinSpec = 0;
}
if ( isEmpty(m_MaxSpec) ) m_MaxSpec = numberOfSpectra-1;
if ( m_MaxSpec > numberOfSpectra-1 || m_MaxSpec < m_MinSpec )
{
g_log.warning("EndSpectrum out of range! Set to max detector number");
m_MaxSpec = numberOfSpectra;
}
if ( m_MinRange > m_MaxRange )
{
g_log.warning("Range_upper is less than Range_lower. Will integrate up to frame maximum.");
m_MaxRange = 0.0;
}
// Create the 1D workspace for the output
MatrixWorkspace_sptr outputWorkspace = API::WorkspaceFactory::Instance().create(localworkspace,m_MaxSpec-m_MinSpec+1,2,1);
Progress progress(this,0,1,(m_MaxSpec-m_MinSpec+1));
PARALLEL_FOR2(localworkspace,outputWorkspace)
// Loop over spectra
for (int i = m_MinSpec; i <= m_MaxSpec; ++i)
{
PARALLEL_START_INTERUPT_REGION
int newindex=i-m_MinSpec;
// Copy over spectrum and detector number info
outputWorkspace->getSpectrum(newindex)->copyInfoFrom(*localworkspace->getSpectrum(i));
// Retrieve the spectrum into a vector
const MantidVec& X = localworkspace->readX(i);
const MantidVec& Y = localworkspace->readY(i);
// Find the range [min,max]
MantidVec::const_iterator lowit, highit;
if (m_MinRange == EMPTY_DBL()) lowit=X.begin();
else lowit=std::lower_bound(X.begin(),X.end(),m_MinRange);
if (m_MaxRange == EMPTY_DBL()) highit=X.end();
else highit=std::find_if(lowit,X.end(),std::bind2nd(std::greater<double>(),m_MaxRange));
// If range specified doesn't overlap with this spectrum then bail out
if ( lowit == X.end() || highit == X.begin() ) continue;
--highit; // Upper limit is the bin before, i.e. the last value smaller than MaxRange
MantidVec::difference_type distmin=std::distance(X.begin(),lowit);
MantidVec::difference_type distmax=std::distance(X.begin(),highit);
MantidVec::const_iterator maxY;
// Find the max/min element
if (showMin==true)
{
maxY=std::min_element(Y.begin()+distmin,Y.begin()+distmax);
}
else
{
maxY=std::max_element(Y.begin()+distmin,Y.begin()+distmax);
}
MantidVec::difference_type d=std::distance(Y.begin(),maxY);
// X boundaries for the max/min element
outputWorkspace->dataX(newindex)[0]=*(X.begin()+d);
outputWorkspace->dataX(newindex)[1]=*(X.begin()+d+1); //This is safe since X is of dimension Y+1
outputWorkspace->dataY(newindex)[0]=*maxY;
progress.report();
PARALLEL_END_INTERUPT_REGION
}
PARALLEL_CHECK_INTERUPT_REGION
// Assign it to the output workspace property
setProperty("OutputWorkspace",outputWorkspace);
return;
}
/** Executes the algorithm
*
* @throw runtime_error Thrown if algorithm cannot execute
*/
void Fit1D::exec() {
// Custom initialization
prepare();
// check if derivative defined in derived class
bool isDerivDefined = true;
gsl_matrix *M = NULL;
try {
const std::vector<double> inTest(m_parameterNames.size(), 1.0);
std::vector<double> outTest(m_parameterNames.size());
const double xValuesTest = 0;
JacobianImpl J;
M = gsl_matrix_alloc(m_parameterNames.size(), 1);
J.setJ(M);
// note nData set to zero (last argument) hence this should avoid further
// memory problems
functionDeriv(&(inTest.front()), &J, &xValuesTest, 0);
} catch (Exception::NotImplementedError &) {
isDerivDefined = false;
}
gsl_matrix_free(M);
// Try to retrieve optional properties
int histNumber = getProperty("WorkspaceIndex");
const int maxInterations = getProperty("MaxIterations");
// Get the input workspace
MatrixWorkspace_const_sptr localworkspace = getProperty("InputWorkspace");
// number of histogram is equal to the number of spectra
const size_t numberOfSpectra = localworkspace->getNumberHistograms();
// Check that the index given is valid
if (histNumber >= static_cast<int>(numberOfSpectra)) {
g_log.warning("Invalid Workspace index given, using first Workspace");
histNumber = 0;
}
// Retrieve the spectrum into a vector
const MantidVec &XValues = localworkspace->readX(histNumber);
const MantidVec &YValues = localworkspace->readY(histNumber);
const MantidVec &YErrors = localworkspace->readE(histNumber);
// Read in the fitting range data that we were sent
double startX = getProperty("StartX");
double endX = getProperty("EndX");
// check if the values had been set, otherwise use defaults
if (isEmpty(startX)) {
startX = XValues.front();
modifyStartOfRange(startX); // does nothing by default but derived class may
// provide a more intelligent value
}
if (isEmpty(endX)) {
endX = XValues.back();
modifyEndOfRange(endX); // does nothing by default but derived class may
// previde a more intelligent value
}
int m_minX;
int m_maxX;
// Check the validity of startX
if (startX < XValues.front()) {
g_log.warning("StartX out of range! Set to start of frame.");
startX = XValues.front();
}
// Get the corresponding bin boundary that comes before (or coincides with)
// this value
for (m_minX = 0; XValues[m_minX + 1] < startX; ++m_minX) {
}
// Check the validity of endX and get the bin boundary that come after (or
// coincides with) it
if (endX >= XValues.back() || endX < startX) {
g_log.warning("EndX out of range! Set to end of frame");
endX = XValues.back();
m_maxX = static_cast<int>(YValues.size());
} else {
for (m_maxX = m_minX; XValues[m_maxX] < endX; ++m_maxX) {
}
}
afterDataRangedDetermined(m_minX, m_maxX);
// create and populate GSL data container warn user if l_data.n < l_data.p
// since as a rule of thumb this is required as a minimum to obtained
// 'accurate'
// fitting parameter values.
FitData l_data(this, getProperty("Fix"));
l_data.n =
m_maxX -
m_minX; // m_minX and m_maxX are array index markers. I.e. e.g. 0 & 19.
if (l_data.n == 0) {
g_log.error("The data set is empty.");
throw std::runtime_error("The data set is empty.");
}
if (l_data.n < l_data.p) {
g_log.error(
"Number of data points less than number of parameters to be fitted.");
throw std::runtime_error(
"Number of data points less than number of parameters to be fitted.");
}
l_data.X = new double[l_data.n];
l_data.sigmaData = new double[l_data.n];
l_data.forSimplexLSwrap = new double[l_data.n];
l_data.parameters = new double[nParams()];
// check if histogram data in which case use mid points of histogram bins
const bool isHistogram = localworkspace->isHistogramData();
for (unsigned int i = 0; i < l_data.n; ++i) {
if (isHistogram)
l_data.X[i] =
0.5 * (XValues[m_minX + i] +
XValues[m_minX + i + 1]); // take mid-point if histogram bin
else
l_data.X[i] = XValues[m_minX + i];
}
l_data.Y = &YValues[m_minX];
// check that no error is negative or zero
for (unsigned int i = 0; i < l_data.n; ++i) {
if (YErrors[m_minX + i] <= 0.0) {
l_data.sigmaData[i] = 1.0;
} else
l_data.sigmaData[i] = YErrors[m_minX + i];
}
// create array of fitted parameter. Take these to those input by the user.
// However, for doing the
// underlying fitting it might be more efficient to actually perform the
// fitting on some of other
// form of the fitted parameters. For instance, take the Gaussian sigma
// parameter. In practice it
// in fact more efficient to perform the fitting not on sigma but 1/sigma^2.
// The methods
// modifyInitialFittedParameters() and modifyFinalFittedParameters() are used
// to allow for this;
// by default these function do nothing.
m_fittedParameter.clear();
for (size_t i = 0; i < nParams(); i++) {
m_fittedParameter.push_back(getProperty(m_parameterNames[i]));
}
modifyInitialFittedParameters(
m_fittedParameter); // does nothing except if overwritten by derived class
for (size_t i = 0; i < nParams(); i++) {
l_data.parameters[i] = m_fittedParameter[i];
}
// set-up initial guess for fit parameters
gsl_vector *initFuncArg;
initFuncArg = gsl_vector_alloc(l_data.p);
for (size_t i = 0, j = 0; i < nParams(); i++) {
if (l_data.active[i])
gsl_vector_set(initFuncArg, j++, m_fittedParameter[i]);
}
// set-up GSL container to be used with GSL simplex algorithm
gsl_multimin_function gslSimplexContainer;
gslSimplexContainer.n = l_data.p; // n here refers to number of parameters
gslSimplexContainer.f = &gsl_costFunction;
gslSimplexContainer.params = &l_data;
// set-up GSL least squares container
gsl_multifit_function_fdf f;
f.f = &gsl_f;
f.df = &gsl_df;
f.fdf = &gsl_fdf;
f.n = l_data.n;
f.p = l_data.p;
f.params = &l_data;
// set-up remaining GSL machinery for least squared
const gsl_multifit_fdfsolver_type *T = gsl_multifit_fdfsolver_lmsder;
gsl_multifit_fdfsolver *s = NULL;
if (isDerivDefined) {
s = gsl_multifit_fdfsolver_alloc(T, l_data.n, l_data.p);
gsl_multifit_fdfsolver_set(s, &f, initFuncArg);
}
// set-up remaining GSL machinery to use simplex algorithm
const gsl_multimin_fminimizer_type *simplexType =
gsl_multimin_fminimizer_nmsimplex;
gsl_multimin_fminimizer *simplexMinimizer = NULL;
gsl_vector *simplexStepSize = NULL;
if (!isDerivDefined) {
simplexMinimizer = gsl_multimin_fminimizer_alloc(simplexType, l_data.p);
simplexStepSize = gsl_vector_alloc(l_data.p);
gsl_vector_set_all(simplexStepSize,
1.0); // is this always a sensible starting step size?
gsl_multimin_fminimizer_set(simplexMinimizer, &gslSimplexContainer,
initFuncArg, simplexStepSize);
}
// finally do the fitting
int iter = 0;
int status;
double finalCostFuncVal;
double dof = static_cast<double>(
l_data.n - l_data.p); // dof stands for degrees of freedom
// Standard least-squares used if derivative function defined otherwise
// simplex
Progress prog(this, 0.0, 1.0, maxInterations);
if (isDerivDefined) {
do {
iter++;
status = gsl_multifit_fdfsolver_iterate(s);
if (status) // break if error
break;
status = gsl_multifit_test_delta(s->dx, s->x, 1e-4, 1e-4);
prog.report();
} while (status == GSL_CONTINUE && iter < maxInterations);
double chi = gsl_blas_dnrm2(s->f);
finalCostFuncVal = chi * chi / dof;
// put final converged fitting values back into m_fittedParameter
for (size_t i = 0, j = 0; i < nParams(); i++)
if (l_data.active[i])
m_fittedParameter[i] = gsl_vector_get(s->x, j++);
} else {
do {
iter++;
status = gsl_multimin_fminimizer_iterate(simplexMinimizer);
if (status) // break if error
break;
double size = gsl_multimin_fminimizer_size(simplexMinimizer);
status = gsl_multimin_test_size(size, 1e-2);
prog.report();
} while (status == GSL_CONTINUE && iter < maxInterations);
finalCostFuncVal = simplexMinimizer->fval / dof;
// put final converged fitting values back into m_fittedParameter
for (unsigned int i = 0, j = 0; i < m_fittedParameter.size(); i++)
if (l_data.active[i])
m_fittedParameter[i] = gsl_vector_get(simplexMinimizer->x, j++);
}
modifyFinalFittedParameters(
m_fittedParameter); // do nothing except if overwritten by derived class
// Output summary to log file
std::string reportOfFit = gsl_strerror(status);
g_log.information() << "Iteration = " << iter << "\n"
<< "Status = " << reportOfFit << "\n"
<< "Chi^2/DoF = " << finalCostFuncVal << "\n";
for (size_t i = 0; i < m_fittedParameter.size(); i++)
g_log.information() << m_parameterNames[i] << " = " << m_fittedParameter[i]
<< " \n";
// also output summary to properties
setProperty("OutputStatus", reportOfFit);
setProperty("OutputChi2overDoF", finalCostFuncVal);
for (size_t i = 0; i < m_fittedParameter.size(); i++)
setProperty(m_parameterNames[i], m_fittedParameter[i]);
std::string output = getProperty("Output");
if (!output.empty()) {
// calculate covariance matrix if derivatives available
gsl_matrix *covar(NULL);
std::vector<double> standardDeviations;
std::vector<double> sdExtended;
if (isDerivDefined) {
covar = gsl_matrix_alloc(l_data.p, l_data.p);
gsl_multifit_covar(s->J, 0.0, covar);
int iPNotFixed = 0;
for (size_t i = 0; i < nParams(); i++) {
sdExtended.push_back(1.0);
if (l_data.active[i]) {
sdExtended[i] = sqrt(gsl_matrix_get(covar, iPNotFixed, iPNotFixed));
iPNotFixed++;
}
}
modifyFinalFittedParameters(sdExtended);
for (size_t i = 0; i < nParams(); i++)
if (l_data.active[i])
standardDeviations.push_back(sdExtended[i]);
declareProperty(
new WorkspaceProperty<API::ITableWorkspace>(
"OutputNormalisedCovarianceMatrix", "", Direction::Output),
"The name of the TableWorkspace in which to store the final "
"covariance matrix");
setPropertyValue("OutputNormalisedCovarianceMatrix",
output + "_NormalisedCovarianceMatrix");
Mantid::API::ITableWorkspace_sptr m_covariance =
Mantid::API::WorkspaceFactory::Instance().createTable(
"TableWorkspace");
m_covariance->addColumn("str", "Name");
std::vector<std::string>
paramThatAreFitted; // used for populating 1st "name" column
for (size_t i = 0; i < nParams(); i++) {
if (l_data.active[i]) {
m_covariance->addColumn("double", m_parameterNames[i]);
paramThatAreFitted.push_back(m_parameterNames[i]);
}
}
for (size_t i = 0; i < l_data.p; i++) {
Mantid::API::TableRow row = m_covariance->appendRow();
row << paramThatAreFitted[i];
for (size_t j = 0; j < l_data.p; j++) {
if (j == i)
row << 1.0;
else {
row << 100.0 * gsl_matrix_get(covar, i, j) /
sqrt(gsl_matrix_get(covar, i, i) *
gsl_matrix_get(covar, j, j));
}
}
}
setProperty("OutputNormalisedCovarianceMatrix", m_covariance);
}
declareProperty(new WorkspaceProperty<API::ITableWorkspace>(
"OutputParameters", "", Direction::Output),
"The name of the TableWorkspace in which to store the "
"final fit parameters");
declareProperty(
new WorkspaceProperty<MatrixWorkspace>("OutputWorkspace", "",
Direction::Output),
"Name of the output Workspace holding resulting simlated spectrum");
setPropertyValue("OutputParameters", output + "_Parameters");
setPropertyValue("OutputWorkspace", output + "_Workspace");
// Save the final fit parameters in the output table workspace
Mantid::API::ITableWorkspace_sptr m_result =
Mantid::API::WorkspaceFactory::Instance().createTable("TableWorkspace");
m_result->addColumn("str", "Name");
m_result->addColumn("double", "Value");
if (isDerivDefined)
m_result->addColumn("double", "Error");
Mantid::API::TableRow row = m_result->appendRow();
row << "Chi^2/DoF" << finalCostFuncVal;
for (size_t i = 0; i < nParams(); i++) {
Mantid::API::TableRow row = m_result->appendRow();
row << m_parameterNames[i] << m_fittedParameter[i];
if (isDerivDefined && l_data.active[i]) {
// perhaps want to scale standard deviations with sqrt(finalCostFuncVal)
row << sdExtended[i];
}
}
setProperty("OutputParameters", m_result);
// Save the fitted and simulated spectra in the output workspace
MatrixWorkspace_const_sptr inputWorkspace = getProperty("InputWorkspace");
int iSpec = getProperty("WorkspaceIndex");
const MantidVec &inputX = inputWorkspace->readX(iSpec);
const MantidVec &inputY = inputWorkspace->readY(iSpec);
int histN = isHistogram ? 1 : 0;
Mantid::DataObjects::Workspace2D_sptr ws =
boost::dynamic_pointer_cast<Mantid::DataObjects::Workspace2D>(
Mantid::API::WorkspaceFactory::Instance().create(
"Workspace2D", 3, l_data.n + histN, l_data.n));
ws->setTitle("");
ws->getAxis(0)->unit() =
inputWorkspace->getAxis(0)
->unit(); // UnitFactory::Instance().create("TOF");
for (int i = 0; i < 3; i++)
ws->dataX(i)
.assign(inputX.begin() + m_minX, inputX.begin() + m_maxX + histN);
ws->dataY(0).assign(inputY.begin() + m_minX, inputY.begin() + m_maxX);
MantidVec &Y = ws->dataY(1);
MantidVec &E = ws->dataY(2);
double *lOut =
new double[l_data.n]; // to capture output from call to function()
modifyInitialFittedParameters(m_fittedParameter); // does nothing except if
// overwritten by derived
// class
function(&m_fittedParameter[0], lOut, l_data.X, l_data.n);
modifyInitialFittedParameters(m_fittedParameter); // reverse the effect of
// modifyInitialFittedParameters - if any
for (unsigned int i = 0; i < l_data.n; i++) {
Y[i] = lOut[i];
E[i] = l_data.Y[i] - Y[i];
}
delete[] lOut;
setProperty("OutputWorkspace",
boost::dynamic_pointer_cast<MatrixWorkspace>(ws));
if (isDerivDefined)
gsl_matrix_free(covar);
}
// clean up dynamically allocated gsl stuff
if (isDerivDefined)
gsl_multifit_fdfsolver_free(s);
else {
gsl_vector_free(simplexStepSize);
gsl_multimin_fminimizer_free(simplexMinimizer);
}
delete[] l_data.X;
delete[] l_data.sigmaData;
delete[] l_data.forSimplexLSwrap;
delete[] l_data.parameters;
gsl_vector_free(initFuncArg);
return;
}
/**
* Execute the algorithm
*/
void ExtractMask::exec()
{
MatrixWorkspace_const_sptr inputWS = getProperty("InputWorkspace");
Geometry::Instrument_const_sptr instr = inputWS->getInstrument();
// convert input to a mask workspace
DataObjects::MaskWorkspace_const_sptr inputMaskWS
= boost::dynamic_pointer_cast<const DataObjects::MaskWorkspace>(inputWS);
bool inputWSIsSpecial = bool(inputMaskWS);
if (inputWSIsSpecial)
{
g_log.notice() << "Input workspace is a MaskWorkspace.\n";
}
DataObjects::MaskWorkspace_sptr maskWS;
// List masked of detector IDs
std::vector<detid_t> detectorList;
if (instr)
{
const int nHist = static_cast<int>(inputWS->getNumberHistograms());
// Create a new workspace for the results, copy from the input to ensure that we copy over the instrument and current masking
maskWS = DataObjects::MaskWorkspace_sptr(new DataObjects::MaskWorkspace(inputWS));
maskWS->setTitle(inputWS->getTitle());
Progress prog(this,0.0,1.0,nHist);
MantidVecPtr xValues;
xValues.access() = MantidVec(1, 0.0);
PARALLEL_FOR2(inputWS, maskWS)
for( int i = 0; i < nHist; ++i )
{
PARALLEL_START_INTERUPT_REGION
bool inputIsMasked(false);
IDetector_const_sptr inputDet;
try
{
inputDet = inputWS->getDetector(i);
if (inputWSIsSpecial) {
inputIsMasked = inputMaskWS->isMaskedIndex(i);
}
// special workspaces can mysteriously have the mask bit set
// but only check if we haven't already decided to mask the spectrum
if( !inputIsMasked && inputDet->isMasked() )
{
inputIsMasked = true;
}
if (inputIsMasked)
{
detid_t id = inputDet->getID();
PARALLEL_CRITICAL(name)
{
detectorList.push_back(id);
}
}
}
catch(Kernel::Exception::NotFoundError &)
{
inputIsMasked = false;
}
maskWS->setMaskedIndex(i, inputIsMasked);
prog.report();
PARALLEL_END_INTERUPT_REGION
}
PARALLEL_CHECK_INTERUPT_REGION
// Clear all the "masked" bits on the output masked workspace
Geometry::ParameterMap & pmap = maskWS->instrumentParameters();
pmap.clearParametersByName("masked");
}
else // no instrument
{
// TODO should fill this in
throw std::runtime_error("No instrument");
void Linear::exec()
{
// Get the input workspace
MatrixWorkspace_const_sptr inputWorkspace = getProperty("InputWorkspace");
// Get the spectrum to fit
const int histNumber = getProperty("WorkspaceIndex");
// Check validity
if ( histNumber >= static_cast<int>(inputWorkspace->getNumberHistograms()) )
{
g_log.error() << "WorkspaceIndex set to an invalid value of " << histNumber << std::endl;
throw Exception::IndexError(histNumber,inputWorkspace->getNumberHistograms(),"Linear WorkspaceIndex property");
}
// Get references to the data in the chosen spectrum
const MantidVec& X = inputWorkspace->dataX(histNumber);
const MantidVec& Y = inputWorkspace->dataY(histNumber);
const MantidVec& E = inputWorkspace->dataE(histNumber);
// Check if this spectrum has errors
double errorsCount = 0.0;
// Retrieve the Start/EndX properties, if set
this->setRange(X,Y);
const bool isHistogram = inputWorkspace->isHistogramData();
// If the spectrum to be fitted has masked bins, we want to exclude them (even if only partially masked)
const MatrixWorkspace::MaskList * const maskedBins =
( inputWorkspace->hasMaskedBins(histNumber) ? &(inputWorkspace->maskedBins(histNumber)) : NULL );
// Put indices of masked bins into a set for easy searching later
std::set<size_t> maskedIndices;
if (maskedBins)
{
MatrixWorkspace::MaskList::const_iterator it;
for (it = maskedBins->begin(); it != maskedBins->end(); ++it)
maskedIndices.insert(it->first);
}
progress(0);
// Declare temporary vectors and reserve enough space if they're going to be used
std::vector<double> XCen, unmaskedY, weights;
int numPoints = m_maxX - m_minX;
if (isHistogram) XCen.reserve(numPoints);
if (maskedBins) unmaskedY.reserve(numPoints);
weights.reserve(numPoints);
for (int i = 0; i < numPoints; ++i)
{
// If the current bin is masked, skip it
if ( maskedBins && maskedIndices.count(m_minX+i) ) continue;
// Need to adjust X to centre of bin, if a histogram
if (isHistogram) XCen.push_back( 0.5*(X[m_minX+i]+X[m_minX+i+1]) );
// If there are masked bins present, we need to copy the unmasked Y values
if (maskedBins) unmaskedY.push_back(Y[m_minX+i]);
// GSL wants the errors as weights, i.e. 1/sigma^2
// We need to be careful if E is zero because that would naively lead to an infinite weight on the point.
// Solution taken here is to zero weight if error is zero, which typically means Y is zero
// (so it is effectively excluded from the fit).
const double& currentE = E[m_minX+i];
weights.push_back( currentE ? 1.0/(currentE*currentE) : 0.0 );
// However, if the spectrum given has all errors of zero, then we should use the gsl function that
// doesn't take account of the errors.
if ( currentE ) ++errorsCount;
}
progress(0.3);
// If masked bins present, need to recalculate numPoints here
if (maskedBins) numPoints = static_cast<int>(unmaskedY.size());
// If no points left for any reason, bail out
if (numPoints == 0)
{
g_log.error("No points in this range to fit");
throw std::runtime_error("No points in this range to fit");
}
// Set up pointer variables to pass to gsl, pointing them to the right place
const double * const xVals = ( isHistogram ? &XCen[0] : &X[m_minX] );
const double * const yVals = ( maskedBins ? &unmaskedY[0] : &Y[m_minX] );
// Call the gsl fitting function
// The stride value of 1 reflects that fact that we want every element of our input vectors
const int stride = 1;
double *c0(new double),*c1(new double),*cov00(new double),*cov01(new double),*cov11(new double),*chisq(new double);
int status;
// Unless our spectrum has error values for vast majority of points,
// call the gsl function that doesn't use errors
if ( errorsCount/numPoints < 0.9 )
{
g_log.debug("Calling gsl_fit_linear (doesn't use errors in fit)");
status = gsl_fit_linear(xVals,stride,yVals,stride,numPoints,c0,c1,cov00,cov01,cov11,chisq);
}
// Otherwise, call the one that does account for errors on the data points
else
{
g_log.debug("Calling gsl_fit_wlinear (uses errors in fit)");
status = gsl_fit_wlinear(xVals,stride,&weights[0],stride,yVals,stride,numPoints,c0,c1,cov00,cov01,cov11,chisq);
}
progress(0.8);
// Check that the fit succeeded
std::string fitStatus = gsl_strerror(status);
// For some reason, a fit where c0,c1 & chisq are all infinity doesn't report as a
// failure, so check explicitly.
if ( !gsl_finite(*chisq) || !gsl_finite(*c0) || !gsl_finite(*c1) )
fitStatus = "Fit gives infinities";
if (fitStatus != "success") g_log.error() << "The fit failed: " << fitStatus << "\n";
else
g_log.information() << "The fit succeeded, giving y = " << *c0 << " + " << *c1 << "*x, with a Chi^2 of " << *chisq << "\n";
// Set the fit result output properties
setProperty("FitStatus",fitStatus);
setProperty("FitIntercept",*c0);
setProperty("FitSlope",*c1);
setProperty("Cov00",*cov00);
setProperty("Cov11",*cov11);
setProperty("Cov01",*cov01);
setProperty("Chi2",*chisq);
// Create and fill a workspace2D with the same bins as the fitted spectrum and the value of the fit for the centre of each bin
const size_t YSize = Y.size();
MatrixWorkspace_sptr outputWorkspace = WorkspaceFactory::Instance().create(inputWorkspace,1,X.size(),YSize);
// Copy over the X bins
outputWorkspace->dataX(0).assign(X.begin(),X.end());
// Now loop over the spectrum and use gsl function to calculate the Y & E values for the function
for (size_t i = 0; i < YSize; ++i)
{
const double x = ( isHistogram ? 0.5*(X[i]+X[i+1]) : X[i] );
const int err = gsl_fit_linear_est(x,*c0,*c1,*cov00,*cov01,*cov11,&(outputWorkspace->dataY(0)[i]),&(outputWorkspace->dataE(0)[i]));
if (err) g_log.warning() << "Problem in filling the output workspace: " << gsl_strerror(err) << std::endl;
}
setProperty("OutputWorkspace",outputWorkspace);
progress(1);
// Clean up
delete c0;
delete c1;
delete cov00;
delete cov01;
delete cov11;
delete chisq;
}
void SANSSolidAngleCorrection::exec() {
// Reduction property manager
const std::string reductionManagerName = getProperty("ReductionProperties");
boost::shared_ptr<PropertyManager> reductionManager;
if (PropertyManagerDataService::Instance().doesExist(reductionManagerName)) {
reductionManager =
PropertyManagerDataService::Instance().retrieve(reductionManagerName);
} else {
reductionManager = boost::make_shared<PropertyManager>();
PropertyManagerDataService::Instance().addOrReplace(reductionManagerName,
reductionManager);
}
// If the solid angle algorithm isn't in the reduction properties, add it
if (!reductionManager->existsProperty("SolidAngleAlgorithm")) {
AlgorithmProperty *algProp = new AlgorithmProperty("SolidAngleAlgorithm");
algProp->setValue(toString());
reductionManager->declareProperty(algProp);
}
MatrixWorkspace_const_sptr inputWS = getProperty("InputWorkspace");
DataObjects::EventWorkspace_const_sptr inputEventWS =
boost::dynamic_pointer_cast<const EventWorkspace>(inputWS);
if (inputEventWS)
return execEvent();
// Now create the output workspace
MatrixWorkspace_sptr outputWS = getProperty("OutputWorkspace");
if (outputWS != inputWS) {
outputWS = WorkspaceFactory::Instance().create(inputWS);
outputWS->isDistribution(true);
outputWS->setYUnit("");
outputWS->setYUnitLabel("Steradian");
setProperty("OutputWorkspace", outputWS);
}
const int numHists = static_cast<int>(inputWS->getNumberHistograms());
Progress progress(this, 0.0, 1.0, numHists);
// Number of X bins
const int xLength = static_cast<int>(inputWS->readY(0).size());
PARALLEL_FOR2(outputWS, inputWS)
for (int i = 0; i < numHists; ++i) {
PARALLEL_START_INTERUPT_REGION
outputWS->dataX(i) = inputWS->readX(i);
IDetector_const_sptr det;
try {
det = inputWS->getDetector(i);
} catch (Exception::NotFoundError &) {
g_log.warning() << "Spectrum index " << i
<< " has no detector assigned to it - discarding"
<< std::endl;
// Catch if no detector. Next line tests whether this happened - test
// placed
// outside here because Mac Intel compiler doesn't like 'continue' in a
// catch
// in an openmp block.
}
// If no detector found, skip onto the next spectrum
if (!det)
continue;
// Skip if we have a monitor or if the detector is masked.
if (det->isMonitor() || det->isMasked())
continue;
const MantidVec &YIn = inputWS->readY(i);
const MantidVec &EIn = inputWS->readE(i);
MantidVec &YOut = outputWS->dataY(i);
MantidVec &EOut = outputWS->dataE(i);
// Compute solid angle correction factor
const bool is_tube = getProperty("DetectorTubes");
const double tanTheta = tan(inputWS->detectorTwoTheta(det));
const double theta_term = sqrt(tanTheta * tanTheta + 1.0);
double corr;
if (is_tube) {
const double tanAlpha = tan(getYTubeAngle(det, inputWS));
const double alpha_term = sqrt(tanAlpha * tanAlpha + 1.0);
corr = alpha_term * theta_term * theta_term;
} else {
corr = theta_term * theta_term * theta_term;
}
// Correct data for all X bins
for (int j = 0; j < xLength; j++) {
YOut[j] = YIn[j] * corr;
EOut[j] = fabs(EIn[j] * corr);
}
progress.report("Solid Angle Correction");
PARALLEL_END_INTERUPT_REGION
}
PARALLEL_CHECK_INTERUPT_REGION
setProperty("OutputMessage", "Solid angle correction applied");
}
